Healthcare decision makers' perspectives on the creation of new genetic counselor positions in North America: Exploring the case for psychiatric genetic counseling
Bibliographic record
Abstract
Mental illnesses are common and highly heritable. Patients and their families want and benefit from receiving psychiatric genetic counseling (pGC). Though the pGC workforce is among the smallest of genetic counseling (GC) specialties, genetic counselors (GCs) want to practice in this area. A major barrier to the expansion of the pGC workforce is limited availability of advertised positions, but it remains unclear why this is the case. We used a qualitative approach to explore drivers for and barriers to the creation of GC positions (including pGC) at large centralized genetic centers in the United States and Canada that offer multiple specialty GC services. Individuals with responsibilities for making decisions about creating new clinical GC positions were interviewed using a semi-structured guide, and an interpretive description approach was used for inductive data analysis. From interviews with 12 participants, we developed a theoretical model describing how the process of creating new GC positions required institutional prioritization of funding, which was primarily allocated according to physician referral patterns, which in turn were largely driven by availability of genetic testing and clinical practice guidelines. Generating revenue for the institution, improving physician efficiency, and reinforcing institutional mission were all regarded as valued outcomes that bolstered prioritization of funding for new GC positions. Evidence of patient benefit arising from new GC positions (e.g., pGC) seemed to play a lesser role. These findings highlight the tension between how institutions value GC (generating revenue, reacting to genetic testing), and how the GC profession sees its value (providing patient benefit, focus on counseling).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.028 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".